Techniques for knowledge graph (KGs) enrichment have been increasingly
crucial for commercial applications that rely on evolving product catalogues.
However, because of the huge search space of potential enrichment, predictions
from KG completion (KGC) methods suffer from low precision, making them
unreliable for real-world catalogues. Moreover, candidate fa